Statistical tools in Research in Applied Medical Sciences

1. Understanding Statistics in Research
Dr. Senthilvel Vasudevan, M.Sc., M.Phil., DST., PGDBS., Ph. D,
Lecturer in Pharmacy (Biostatistics),
Dept. of Pharmacy Practice,
College of Pharmacy,
KSAU-HS,
Riyadh, Saudi Arabia.

2. What is Statistics?
• The science of collecting, analyzing and
making inference from the collected data.
• It is called as science and it is a tool.

3. Statistics (Contd…)
There are two words in Statistics
1. Statistic 2. Statistics
• Statistic: It means a measured (or) counted
fact (or) piece of information stated as figure
• Height of one person, birth of a baby, etc.,
• Statistics: It is also called Data. It is Plural.
Stated in more than one figures
• Height of 2 persons, birth of 5 babies etc. They
are collected from experiments, records, and
surveys.

4. Definition of Statistics
Statistics means that the collection of data, tabulation,
analysis and interpretation of quantitative information.
• In other words, Statistics is a branch of science used in
dealing with phenomena that can be described
numerically by counts or by measurements.
• Quantitative information: population, birth, fertility,
production and etc.

5. Definition of Statistics (contd…)
Statistics : used in two sense viz., singular and plural.
• In singular noun, it deals with collection, analysis
and interpretation of quantitative information.
• In plural sense, statistics denote some numerical
data such as birth, death etc.

6. Statistics is used in many fields
Medical Statistics
Agricultural Statistics
Educational Statistics
Mathematical Statistics
And so on………….
Bio-Statistics

7. Definition of Bio-Statistics
 Bio-statistics: means when we use the statistical
tools on the Biological Problems and derived some
results about that. Example: Medical Science
 It is also called Bio-metry. It means measurement
of life.
 Normally, in medicine for precision, facts,
observations or measurements have to be
expressed in figures.
 Bar diagrams, Multiple Bar diagram,Histogram, Pie
chart and etc.,

8. Bio-Statistics in Various areas
• In Public Health or Community Health,
it is called Health Statistics.
• In Medicine, it is called Medical
Statistics. In this we study the defect,
injury, disease, efficacy of drug, Serum
and Line of treatment, etc.,
• In population related study it is called
Vital Statistics. e.g. study of vital
events like births, marriages and
deaths.

9. Types of Statistics
STATISTICS
Descriptive Inferential

10. Types of Statistics (Contd…)
• Descriptive Statistics:
Once the data have been collected, we can organize and
summaries in such a manner as to arrive at their orderly
presentation and conclusion. This procedure can be called
Descriptive Statistics.
• Inferential Statistics:
The number of birth and deaths in a state in a particular year.

11. Types of Data
Qualitative Data Quantitative Data
Nominal Ordinal
Discrete Continuous
Interval Ratio

12. Variable: A characteristic that varies from one
biological entity to another is termed as variable.
Various Measurements:
Ratio Scale:
The measurements scales having a constant size interval and
true zero point are said to be ratio of measurement.
Besides heights and numbers, ratio scales include weights
(mg, g), volumes (cc, cu.m), capacities (ml, l), rates (cm/sec.,
Km/h) and lengths of time (h, Yr) etc.,
Measurement Scale

13. Interval Scale:
Some measurement scales posses a constant interval
size but not a zero, they are called internal scales.
A good example is that of the two common
temperature scales.
Example: Celcius (C ) and Fahrenheit (F).
We can find the same difference exists between 25
degree celcius and 30 degree celcius as between 10
degree celcius and 15 degree celcius.

14. Ordinal Scale:
The data consist of an ordering or ranking of
measurement and are said to be on an ordinal scale.
For example: The examination marks of 75, 80, 87,
92, and 95% (ratio scale) might be recorded as A, B,
C, D and E (ordinal scale) respectively.

15. Nominal Scale:
The variables are classified by some quality rather
than by a numerical measurement. In such cases,
the variable is called an attribute and said to using a
nominal scale of measurement.
Examples:
1. Data are represented as male or female.
2. Heights may be recorded as tall or short.

16. Types of Data (contd…)
Quantitative Data
There is a natural numeric scale (numerical Value)
(It can be subdivided into interval and ratio data )
Example: age, height, weight
Qualitative Data
Measuring a characteristic for which there is no natural
numeric scale
( can be subdivided into nominal and ordinal data )
Example: Gender, Eye color

17. Definition of Biostatistics
• The statistical methods applied to biological problems is called
as Biostatistics.
• It is also called Biometry.
• Biometry means Biological Measurement or Measurement of
Life.
Statistical Method: It refers Both data and Methods

18. Methodology in Statistics
Ask a Question
Decide what to
Measure and
How
Choose Method
and Collect Data
Summarize
Data
Analyze Data
Draw
Conclusions/
Interpret Results

19. STATISTICAL METHODS IN RESEARCH
Croxton and Gowden said Statistical Method means “ the
collection, presentation, analysis and interpretation of
numerical data”.
1. Collection of Data:
It is the first step in collection of data. Careful planning is
essential before collecting the data.

20. Statistical Methods (contd…)
2. Presentation of Data:
• The mass data collected should be presented in a suitable
form for further analysis.
• The collected data may be presented in the form of tabular or
diagrammatic or graphical form.
3. Analysis of Data:
The data presented should be carefully analyzed from the
presented data such as measures of central tendencies,
dispersion, correlation, regression, etc.

21. Statistical Methods (Contd…)
4. Interpretation of Data:
• The final step is drawing conclusion from the data collected.
• A valid conclusion must be drawn on the basis of analysis.
• A high degree of skill and experience is necessary for the
interpretation.

22. Sample size calculation
4 x p x q
n = ------------
d x d
p = prevalence in (%)
q = 100 – p
d = allowable error (10% of prevalence)

23. Uses of Statistics in Research
o To calculate average, median, mode standard
deviation of the given collected data
o To compare two sets of data (t – test, F – test)
o To get a conclusion (or) result
o To find the association between the two variables
(Chi-Square test)
o To find the correlation bet. the two variables
r – value in between -1 and +1
o To give the results in a tabular or diagrammatic
form.

24. Measures of Central Tendency
• Mean - average (or) A.M
• Median - Positional average (Middle value)
• Mode – Most repeated value ( The frequent
value)

25. Mean:
Merits
• Mean can never be biased. It is easy to calculate.
• It is least affected by fluctuations of sampling.
• It is based on all the observations of a series. Therefore, it is a
most representative measure.
Demerits
• It is greatly affected by extreme fluctuations. Thus it is not a
true representative value of all the items of the series.
• A.M cannot be used for qualitative characteristics such as
colour of flowers, sweetness of orange or darkness of the
colour.

26. Median:
Merits
• Easy to calculate and understand
• It is not affected by extreme observations
• It is best measure for qualitative data.
Demerits
• It cannot be determined in the case of even number of
observations. We merely estimate it as the arithmetic mean
of the two middle terms.
• It is a positional average. It cannot be accepted for each and
every observation.

27. Mode: Merits
• It easy to calculate and understand
• It is not affected by extreme observations
• It can be calculated from a grouped frequency distribution.
Demerits:
• Mode is not rigidly defined.
• As compared to mean, mode is affected to a great extent by
the fluctuations of sampling.

28. Measures of variability
(or)
Measures of Dispersion
• Range
• Standard Deviation
• Mean Deviation
• Quartile Deviation
• Variance
• Co-efficient of variation

29. Range: Max. value – Min. value
Example: 96 - 34 = 62
Standard Deviation: Root Mean Square
Deviation
Variance:

30. Mean Deviation:
Quartile Deviation:
Coefficient of Variation:

31. Correlation
• describes the degree of relationship between two
variables.
• To define correlation is the average relationship
between two or more variables.
• When the change in one variable makes or causes a
change in other variable then there is a correlation
between these two variables.
• These correlated variables can move in the same
direction or they can move in opposite direction.
• Not always there is a cause and effect relationship
between the variables when there is a change; that
might be due to uncertain change.

32. Correlation (Contd…)
• Simple Correlation is a correlation between
two variables only; meaning the relationship
between two variables.
• Event correlation and simple event correlation
are the types of correlations mainly used in
the industry point of view.

33. Types of Correlation
(1) Positive Correlation
(2) Negative Correlation
(3) Perfectly Positive Correlation
(4) Perfectly Negative Correlation
(5) Zero Correlation
(6) Linear Correlation

34. Positive Correlation
• When two variables move in the same direction then the
correlation between these two variables is said to be Positive
Correlation.
• When the value of one variable increases, the value of other
value also increases at the same rate.
For example: the training and performance of employees in a
company.
Negative Correlation
• In this type of correlation, the two variables move in the
opposite direction.
When the value of a variable increases, the value of the other
variable decreases.
For example: the relationship between price and demand.

35. Perfect Positive Correlation
When there is a change in one variable, and if
there is equal proportion of change in the other
variable say Y in the same direction, then these two
variables are said to have a Perfect Positive Correlation.
Perfectly Negative Correlation
Between two variables X and Y, if the change in X
causes the same amount of change in Y in equal
proportion but in opposite direction, then this
correlation is called as Perfectly Negative Correlation.

36. Zero Correlation
• When the two variables are independent and the
change in one variable has no effect in other
variable, then the correlation between these two
variable is known as Zero Correlation.
Linear Correlation
• If the quantum of change in one variable has a ratio
of change in the quantum of change in the other
variable then it is known as Linear correlation.

37. THANK YOU